Privacy-aware in-network personalization system

ABSTRACT

A personalization system includes a preprocessing component configured to receive a request from a user over a communications network and generate a request key using predefined attributes of the request. A categorization component is configured to map the request key to a subset of domain-dependent vocabulary. An augmentation and buffer component is configured to augment the request with the subset of domain-dependent vocabulary mapped to the request key by the categorization component and to buffer request sequences in queues according to sequence identifiers. An embedding model component is configured to update an embedding model using the buffered request sequences. A personalization component is configured to provide a personalization using the updated embedding model.

FIELD

The present invention relates to a personalization system integratedwithin a working communication network infrastructure which preservesprivacy of its users. The system can adapt the behavior of the networkbased on user activity.

BACKGROUND

The service and content personalization economy is growing at astaggering pace. Global total internet advertising revenue alone isforecast to grow from USD 135.42 bn in 2014 to USD 239.87 bn in 2019,with an annual growth rate of 12.1% over that period. It is projected toexceed TV advertisement to become the largest single advertisingcategory by the year 2019. Even though network operators provide theinfrastructure on which the companies in the personalization domain runtheir business, most operators are not attempting to partake in thisbusiness model.

The earliest work relating to the present invention includespersonalized search and recommendation systems. In order to tailorsearch results to different users, search engine companies, such asGOOGLE and YANDEX, used their search logs containing user browsingbehavior to predict the interests of the different users. Similarly,electronic commerce enterprises, such as AMAZON, used shopping historiesof different consumers to infer interests of the different consumers forfurther recommendation. Typically, both personalized search andrecommendation systems such as these require specific user input (e.g.,user profile and shopping history for product recommendation,browsing/search history for personalization search).

Various publications describe work relating to categorization andon-line advertising. Toubiana, et al., “Adnostic: Privacy PreservingTargeted Advertising,” NDSS 2010 describe a tool that is designed forpersonalized web advertising with the concern of privacy preservation.The domain-related categorization of Adnostic simply depends on thecosine similarity between Google Ads Preferences categories, names andtags of the concerned web page. The focus of Adnostic is privacypreservation through exploring user profiles/interests and thenreporting which ad was viewed without revealing this to the broker.Similarly, with the concern of privacy, Kazienko, et al.,“AdROSA-adaptive personalization of web advertising,” InformationSciences, 177(11):2269-2295, 2007 address the problem of web banneradvertisements personalization with respect to user privacy wherein noneof the user's personal information are stored locally. It is based onextracting knowledge from the web page content and historical usersessions as well as the current behavior of the on-line user, usingdata-mining techniques. Heer, et al., “Separating the swarm;categorization methods for user sessions on the web,” CHI 2002 propose acategorization method using a clustering algorithm which aims toincrease processing efficiency by simply using the features of user viewand visit paths without considering web page content. While this couldimprove the efficiency to some extent, it certainly would lead toinformation loss.

U.S. Patent Application Publication No. 2010/0082435 describes acustomizable ad marker and U.S. Pat. No. 8,521,892 describes a methodand apparatus for controlling web page advertisement through incentivesand restrictions. The systems described here only consider requests forone particular webpage (URL) and do not learn a machine learningembedding model based on request traces. Further, the systems do nothave any capabilities for querying a embedding model learned fromfixed-size sequences of request. Moreover, the systems are focusedsolely on advertisements of a single user, whereas, in contrast,embodiments of the present invention focus on service personalizationbased on request histories by several users. In further contrast,embodiments of the present invention also apply to various types ofnetwork traffic and are not restricted to only webpage request traffic,or in other words, HTTP traffic as specified in the prior art systems.

In sum, none of the existing approaches have addressed the problem by acomprehensive in-network system based on learning privacy-protectingembedding models from request sequences.

SUMMARY

In an embodiment, the present invention provides a privacy-awarein-network personalization system. A preprocessing component isconfigured to receive a request from a user over a communicationsnetwork and generate a request key using predefined attributes of therequest. A categorization component is configured to map the request keyto a subset of domain-dependent vocabulary. An augmentation and buffercomponent is configured to augment the request with the subset ofdomain-dependent vocabulary mapped to the request key by thecategorization component and to buffer request sequences in queuesaccording to sequence identifiers. An embedding model component isconfigured to update an embedding model using the buffered requestsequences. A personalization component is configured to provide apersonalization using the updated embedding model.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be described in even greater detail belowbased on the exemplary figures. The invention is not limited to theexemplary embodiments. Other features and advantages of variousembodiments of the present invention will become apparent by reading thefollowing detailed description with reference to the attached drawingswhich illustrate the following:

FIG. 1 is a schematic view of an exemplary system architecture of apersonalization system according to an embodiment of the invention;

FIG. 2 is a schematic illustration of a request embedding model as aneural network; and

FIG. 3 shows a two-dimensional embedding of example requests.

DETAILED DESCRIPTION

The inventors have recognized a number of reasons why network operatorshave not been involved in the personalization domain. The main challengeis a lack of technological solutions that are (a) efficient for thenetwork setting, (b) effective for personalization problems and (c)sufficiently privacy-preserving. Such technology for efficient andeffective network data monetization would allow network operators topartake in a growing multi-billion dollar economy.

In an embodiment, the present invention addresses this problem byproviding a privacy-preserving in-network personalization system. Thesystem addresses several technological challenges. First, it provides acomprehensive system that can be integrated into a working communicationnetwork infrastructure and changes the network's behavior based on useractivity. For instance, the network speed can be adapted to users;different content can be cached; different advertisements can bedisplayed to users, etc. Second, the system is highly efficient. Thepersonalization is performed by mapping the network users to theirinterests via an embedding model that learns which requests indicatewhich interest. The learning of the embedding model permitspersonalizations to be carried out faster and more accurately than washeretofore possible while at the same time using less computationalresources to so. The mapping of users to interests can be performed inconstant time. Third, the system is privacy-aware. Even though theembedding model at the core of the system is learned using requestsequences associated with users, the learned model does not revealpersonal information about the users in the network. The model can alsobe used to overcome the sparsity and effectiveness problem. By learningwhich requests are associated with which interests using billions ofpast request sequences, it is possible to provide a higher qualitypersonalization experience while using less memory and processing powercompared to the state of the art systems.

In one embodiment, the input of the privacy-aware in-networkpersonalization system consists of sequences of content requests, suchas HTTP and HTTPS requests. The output is a mapping from users topersonalized network services and/or personalized content (such asadvertisements) where the mapping is computed in real-time. The mappingis maintained in a machine learning model that is privacy-aware, thatis, leakage of the model preserves the privacy of the end users of thenetwork for which personalization is provided. The requests themselvesare never stored in the system. The advantages of the proposed systemare efficiency and privacy-awareness. The system can personalize thein-network user experience using constant-time look ups. Moreover, thesystem never stores a mapping between user identifiers (such as IPs,etc.) and their interests.

In contrast, to the state of the art discussed above, an embodiment ofthe present invention maintains a privacy-aware machine learning modelfor user-defined request key and sequence identifiers. The model can beused for personalization problem such as targeted advertisement, contentpersonalization, and service personalization. State of the art systemsare not privacy-aware, do not use an embedding method, and do not workfor request types other than HTTP. Embodiments of the system in thepresent invention are highly efficient and effective at computing themapping from requests to domain-dependent vocabulary terms. The methodsused by the system achieves a high accuracy while remaining tractable ina high-speed communication network setting.

FIG. 1 depicts an overall framework of the proposed in-networkpersonalization system 10, which infers the users' domain-relatedinterests based on network requests 11, such as HTTP and HTTPS requests.The system 10 is situated in a communication network 12, connected to aplurality of computers, tablets smartphones and other internet-capableuser devices 13. The system 10 has six main components: Preprocessing14, Categorization 16, Augmentation and Buffer 18, Embedding Model 20,Personalization 22, and User Interaction 24.

In the following, each of these system components are described:

Preprocessing: There are two inputs to the Preprocessing component 14:(a) an operator provided set of request attributes which are used toform the request key and (b) the request sequences passing through thecore network. The attributes forming a request key are fixed and aretaken from the properties of the types of requests. Which of theattributes form a key is application dependent and can be determined bythe operator of the system. For example, for HTTP requests 11, a requestkey could consist of the domain and content type of the HTTP request. Arequest key value is a particular instantiation of the request key. Forthe example, this could for instance be [www.rei.com, text/html].

Example 1: HTTP Request

[1425291195, 1425291300, 1035, 202.201.13.20, ‘28066’, text/html,http://m.rei.com,http://www.rei.com/s/mens-jackets-deals?ir=collection%3Amens-jackets-deals&page=1]

The request key is used to identify unique requests. Requests with thesame request key are treated as unique in the Embedding Model component20. Consecutive requests with identical request key values are mergedinto one single request.

Categorization: The Categorization component 16 has two inputs: (a) theoperator provided request key and (b) a fixed domain-dependentvocabulary (an element of which is called a term) describing the domainof interest. For instance, for advertisement personalization, thevocabulary could consist of product-related category and product names(sports, iPhone, shoes, electronics, etc.) and for servicepersonalization in a mobile network the vocabulary might consist of thedifferent types of voice and data plans and add-ons (Flat500 MB,VoiceFlat, StudentPlan, . . . ) of the operator.

The Categorization component 16 maps each of the unique request keyvalues to a subset of the domain-dependent vocabulary. The mapping iseither given or can be computed in regular intervals. For instance, forHTTP requests 11, where the request key contains a hostname or URL, aprior crawling of webpages and the computation of an intersection of thewebpage text and the domain-dependent vocabulary results in such amapping. For service personalization in mobile networks, the mappingmight be determined based on subscriber information. This mapping is aninitial mapping that is refined in the Embedding model component 20.

The mapping of request keys to sets of domain-dependent vocabulary termsis stored in a table so as to facilitate constant-time lookup.

Example 2: Categorization

For HTTP requests and the request key consisting of the URL path and thecontent type, request key=[URL path, content type], the system uses textin the webpages to map each request key value to a set ofdomain-dependent terms. Particular content types might also lead to theaddition of terms (here: video-affine).

[[request key, subset of vocabulary]][[www.rei.com/c/mens-running-shoes/, text/html], {outdoors, sports,running, shoes}][[www.thetannery.com/men/, text/html], {male, fashion}][[www.rei.com, video/mpeg], {video-affine, outdoors}]

For several request keys, the set of vocabulary terms might be empty.The Embedding Model component 20 is used to overcome the problem ofsparse population of request key values. The Categorization component 16can be privacy-preserving by not permitting any user-specific data to bestored as part of the component.

Augmentation and Buffer: The Augmentation and Buffer component 18 hastwo inputs: (a) The mapping from request key values to the set ofdomain-dependent terms 17 as maintained in the Categorization component16 and (b) a sequence of incoming preprocessed requests 19 from thePreprocessing component 14. The task of the Augmentation and Buffercomponent is to augment the requests 19 (identified by the request keyvalue) with the set of vocabulary terms maintained by the Categorizationcomponent 16. Moreover, the Augmentation and Buffer component alsobuffers request sequences which are divided up according to sequenceidentifiers (SIDs). Sequence identifiers indicate requests that are(with high probability) made by the same individual user. A typicalchoice for sequence identifiers in communication networks would be theIP address or internal user ids. The downstream Embedding Modelcomponent 20 accepts fixed-length sequences of requests, where each suchsequence is made by an individual user with high probability. Let thelength of the requests be L and an odd integer number. By using thesequence identifiers, the Augmentation and Buffer component 18 dividesthe original request sequence into request subsequences and buffersthese sequences using one queue per sequence identifier. Whenever aqueue has reached length L, the queue content is sent to the EmbeddingModel component 20 as sequences 21 and the oldest element of the queueis removed. The sequences 21 sent to the Embedding Model component 20are used to update the embedding model.

In an embodiment, the Augmentation and Buffer component 18 has anadditional important function. Specifically, it maintains a counter thatkeeps track, for each request key value, the number of sequenceidentifiers that have made a request to that request key value. Onlywhen the counter exceeds a user-given integer threshold k>1 (say 5),requests with this request key are augmented and buffered. This ensurestwo properties of the embedding model. First, it enforces the embeddingmodel to satisfy k-anonymity: the information for each sequenceidentifier contained in the embedding model cannot be distinguished fromat least k−1 sequence identifiers whose information also appears in therelease. Second, it enables the exclusion of rare request key valuesthat might introduce noise into the embedding model.

The following example demonstrates a typical input and output of theAugmentation and Buffer component 18.

Example 3

Input and output of the Augmentation and Buffer component 18 withrequest key comprised of URL path and content type. Here, the SID is theIP address.

Input: (request sequence with request key = [URL path, content type]) .. . 20.21.13.2 [www.rei.com/c/mens-running-shoes/, text/html] 20.21.14.2[www.thetannery.com/men/, text/html] 20.21.13.2 [www.rei.com,video/mpeg] 24.22.13.2 [www.adidas.com/us/basketball-shoes/, text/html]20.21.14.2 [www.thetannery.com/men/, video/mpeg] 24.22.13.2[www.amazon.com/dp/B00I15SB16/, text/html] . . . Output: Buffer 1: . . .20.21.13.2 [[www.rei.com/c/mens-running-shoes/, text/html], {outdoors,sports, running, shoes}] 20.21.13.2 [[www.rei.com, video/mpeg],{video-affine, outdoors}] 20.21.13.2 . . . . . . Buffer 2: . . .20.21.14.2 [[www.thetannery.com/men/, text/html], {male, fashion}]20.21.14.2 [[www.thetannery.com/men/, video/mpeg]], {video-affine, male,fashion}] 20.21.14.2 . . . . . . Buffer 3: . . . 24.22.13.2[[www.adidas.com/us/basketball-shoes/, text/html], {sports, basketball,shoes}] 24.22.13.2 [[www.amazon.com/dp/B00I15SB16/, text/html],{electronics, tablet, kindle}] 24.22.13.2 . . . . . .

Embedding Model: The Embedding Model component 20 maintains andcontinuously updates a machine learning model that drives the proposedin-network personalization system. It uses the incoming stream ofsequences 21 from the Augmentation and Buffer component 18 to update anembedding model. The embedding model is based on shallow neural networkswhich are mostly trained using stochastic gradient descent (SGD) andback-propagation (see Rumelhart, et al., “Learning internalrepresentations by back-propagating errors,” Nature, 323:533.536, 1986)due to the method's efficiency. The embedding model transforms a vectorrepresentation of tokens into a lower dimensional dense vectorrepresentation. Such embedding models have been applied to naturallanguage processing (NLP) problems (where tokens correspond to words) indifferent ways as discussed in Mikolav, et al., “Efficient estimation ofword representations in vector space,” arXiv: 1301.3781, 2013 andMikolav, et al., “Distributed representations of words and phrases andtheir compositionality,” Advances in neural information processingsystems, 2013. However, to date, such models have not been applied toany application in network request sequences.

An embodiment of the proposed request embedding model in the presentinvention has two distinct sets of tokens: (1) The set of input tokensis the set of request key values visited at least k times (see thedescription of the Augmentation & Buffer component 18) and (2) the setof output tokens is here the domain-dependent vocabulary (see thedescription of the Categorization component 16).

In the Embedding Model component 20, the request sequences 21 providedby the Augmentation & Buffer component 18 are used as contexts of therequest key value situated at the center of the sequence. Contrary toword embedding models, these contexts contain the center request keyvalue itself. The output of the model is the union of the setsdomain-dependent terms associated with the context request key values.

According to an embodiment, the Embedding Model component 20 operates asfollows:

The Embedding Model component 20 generates a one hot encoding of the setof all request key values. The dimension D of the vector space encodingis the number of request key values. A request key value is representedby a one hot vector, that is, a vector of length D with all zero entriesexcept an entry of value 1 for the position corresponding to theparticular request key value. The component also generates a one hotencoding for the domain-dependent vocabulary terms.

The parameter L (see the description of the Augmentation and Buffercomponent 18) determines the size of the context used in the embeddingmodel, that is, the number of requests before and after the currentrequest that is used as input to the model. The context size c is set toc=(L−1)/2 (see FIG. 2).

The output layer (see FIG. 2) corresponds to the one hot encodings ofthe domain-dependent terms. For each context with center request keyrkv, the model is trained to predict the one hot encoding of thedomain-dependent terms for rkv.

The weights W₁ and W₂ (see FIG. 2) are updated using back-propagationand SGD. One can use several loss functions such as the one resultingfrom Negative Sampling (see Rumelhart, et al., “Learning internalrepresentations by back-propagating errors,” Nature, 323:533.536, 1986).

At each point in time, the embedding model provides an embedding of therequest key values. FIG. 3 illustrates a two-dimensional embedding onthe example requests. The closer two request key values in the embeddingspace, the more likely they have been made by individuals with similarpersonalization needs. For the request key indicated with the (red) staron the second line, the two closest requests are indicated with indented(green) stars above and below it.

Given an arbitrary request key value rkv, the system determines the mnearest neighbors of rkv and takes the union of all sets ofdomain-dependent terms associated with these neighbors. This can be donewith existing efficient m-nearest neighbor algorithms andimplementations. A look-up table is updated that stores a mapping fromthe request key values to the set of domain-dependent terms computed inthis way. This table is used by the Personalization component 22described below.

Personalization: The Personalization component 22 takes incomingrequests 23 and performs a look-up in the table that stores the mappingfrom request key values to subsets of the domain-dependent vocabulary.This look-up is possible in constant time and allows the system toprocess request sequences at network speed.

User Interaction: The User Interaction component 24 detects how the userreacts to the personalization 25 (e.g., whether the user clicks on arecommended ad or not) and updates the mapping from request key valuesto domain-dependent vocabulary accordingly. For instance, if aftervisiting a URL U₁ and being presented with Ad A₁ the user never clickson it, the keywords associated with the ad are removed from the set ofterms associated with U₁ in the Categorization component 16 via feedback26.

EXAMPLE EMBODIMENTS

In the following, two particular, concrete embodiments of the proposedin-network personalization system are described.

First Embodiment

An in-network personalized advertising system. In this case, thedomain-dependent vocabulary consists of product-related keywords. Sincethe system maps network requests to sets of product-related keywords,the system will be used to automatically inject advertisements relatedto these keywords in mobile devices, by issuing additional requests fromthe network. This changes the behavior of the communication network on aper-user level and tailors advertisements to the current users based ontheir requests. A brokerage system that allows advertisers to bid onkeywords can be used to connect the output of the proposed system toadvertisements.

Second Embodiment

An in-network personalized quality of service system. In this case, thedomain-dependent vocabulary consists of terms that are associated withparticular user types (for instance, user who watches mostly videos,user who checks mostly e-mails, etc.). Since the proposed system mapsusers to their request behavior type, the network behavior can bechanged according to the type of user identified. For instance, thenetwork can have different caching strategies based on the type ofusers. Users who request frequent video content can be assigned morebandwidth. Users who use real-time applications such as navigationservices can be assigned lower latency by placing the application'salgorithm closer to the edge.

According to different embodiments, the system provides for thefollowing features/advantages: A method that performs a separation of acontinuous request sequence according to predefined sequence identifiers(SID) and request key values; buffering of fixed-size request sequencesfor each SID; and/or online learning of embedding model using thebuffered fixed-size sequences, but only if a request key value has beenused by a number of different users to ensure privacy.

In an embodiment, the present invention also provides a method for userpersonalization comprising the steps of:

pre-processing an incoming request sequence per predefined request keyvalues;

separating the request sequence according to predefined sequenceidentifier (SID) and request key values;

buffering of per SID request sequences;

online learning of an embedding model from the buffered requestsequences;

changing personalization and network characteristics based on queries tothe embedding model; and

updating the mapping in the Categorization component via a feedbackloop.

Embodiments of the present invention are useable, for example, in aTraffic Management Solution product suite currently being marketed byNEC. More generally, the system can be integrated within anycommunication network system.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive. Itwill be understood that changes and modifications may be made by thoseof ordinary skill within the scope of the following claims. Inparticular, the present invention covers further embodiments with anycombination of features from different embodiments described above andbelow. Additionally, statements made herein characterizing the inventionrefer to an embodiment of the invention and not necessarily allembodiments.

The terms used in the claims should be construed to have the broadestreasonable interpretation consistent with the foregoing description. Forexample, the use of the article “a” or “the” in introducing an elementshould not be interpreted as being exclusive of a plurality of elements.Likewise, the recitation of “or” should be interpreted as beinginclusive, such that the recitation of “A or B” is not exclusive of “Aand B,” unless it is clear from the context or the foregoing descriptionthat only one of A and B is intended. Further, the recitation of “atleast one of A, B and C” should be interpreted as one or more of a groupof elements consisting of A, B and C, and should not be interpreted asrequiring at least one of each of the listed elements A, B and C,regardless of whether A, B and C are related as categories or otherwise.Moreover, the recitation of “A, B and/or C” or “at least one of A, B orC” should be interpreted as including any singular entity from thelisted elements, e.g., A, any subset from the listed elements, e.g., Aand B, or the entire list of elements A, B and C.

What is claimed is:
 1. A personalization system, comprising: apreprocessing component configured to receive a request from a user overa communications network and generate a request key using predefinedattributes of the request; a categorization component configured to mapthe request key to a subset of domain-dependent vocabulary; anaugmentation and buffer component configured to augment the request withthe subset of domain-dependent vocabulary mapped to the request key bythe categorization component and to buffer request sequences in queuesaccording to sequence identifiers; an embedding model componentconfigured to update an embedding model using the buffered requestsequences; and a personalization component configured to provide apersonalization using the updated embedding model.
 2. Thepersonalization system according to claim 1, wherein the embedding modelcomponent is configured to output a union of the domain-dependentvocabulary associated with request key values of the buffered requestsequences.
 3. The personalization system according to claim 1, whereinthe embedding model uses two distinct sets of tokens including a set ofinput tokens, which is a set of request key values visited at least apredetermined number of times by different users, and a set of outputtokens, which comprises the corresponding domain-dependent vocabulary.4. The personalization system according to claim 1, further comprising auser interaction component configured to detect whether thepersonalization was utilized by the user and to provide feedback aboutthe utilization for updating a table used by the categorizationcomponent for the mapping.
 5. The personalization system according toclaim 1, wherein the sequence identifiers each correspond to anindividual user, and wherein the augmentation and buffer componentincludes a counter configured to count, for each request key value, anumber of the sequence identifiers which have made a request to therequest key value.
 6. The personalization system according to claim 5,wherein the augmentation and buffer component is configured to augmentand buffer the requests only upon the counter reaching a predeterminedthreshold of the sequence identifiers which have made the request to therequest key value so as to ensure privacy.
 7. The personalization systemaccording to claim 1, wherein the request sequences are buffered usingone queue per sequence identifier, and wherein the augmentation andbuffer component is configured to transfer the queues to the embeddingmodel upon the queues having reached a predetermined, fixed length. 8.The personalization system according to claim 1, wherein thepersonalization is an advertisement.
 9. A method for userpersonalization, comprising: generating a request key using predefinedattributes of a request received from a user over a communicationsnetwork; mapping the request key to a subset of domain-dependentvocabulary stored in a look-up table; augmenting the request with thesubset of domain-dependent vocabulary mapped to the request key;buffering request sequences in queues according to sequence identifiers;updating an embedding model using the buffered request sequences; andproviding a personalization to the user using the updated embeddingmodel.
 10. The method according to claim 9, wherein the embedding modeluses two distinct sets of tokens including a set of input tokens, whichis a set of request key values visited at least a predetermined numberof times by different users, and a set of output tokens, which comprisesthe corresponding domain-dependent vocabulary.
 11. The method accordingto claim 9, further comprising detecting whether the personalization wasutilized by the user, providing feedback about the utilization andupdating the look-up table based on whether the personalization wasutilized or not.
 12. The method according to claim 9, wherein thesequence identifiers each correspond to an individual user, the methodfurther comprising using a counter to count, for each request key value,a number of the sequence identifiers which have made a request to therequest key value.
 13. The method according to claim 12, wherein therequest sequences are buffered until the counter reaches a predeterminedthreshold of the sequence identifiers which have made the request to therequest key value so as to ensure privacy.
 14. The method according toclaim 9, wherein the request sequences are buffered using one queue persequence identifier, and wherein the augmentation and buffer componentis configured to transfer the queues to the embedding model upon thequeues having reached a predetermined, fixed length.
 15. Anon-transitory, computer-readable medium having instructions thereon,which when executed on one or more processors causes the processors toperform a method comprising: generating a request key using predefinedattributes of a request received from a user over a communicationsnetwork; mapping the request key to a subset of domain-dependentvocabulary stored in a look-up table; augmenting the request with thesubset of domain-dependent vocabulary mapped to the request key;buffering request sequences in queues according to sequence identifiers;updating an embedding model using the buffered request sequences; andproviding a personalization to the user using the updated embeddingmodel.